Data Scientist

• Coach, mentor, and manage a world-class team of Partner Development Managers
• Own and execute a forward-thinking partner strategy focused on innovative ISVs and Data Providers
• Build deep relationships with Snowflake’s sales teams and drive joint sales initiatives
• Collaborate cross-functionally with Product, Industry GTM, Marketing, Operations, and Sales teams
• Track team performance, partner success metrics, and customer impact

Manager, Data Cloud Partners – Manufacturing

• Own assigned projects and workstreams from initiation through closeout.
• Develop and maintain project execution plans, schedules, and milestone tracking.
• Drive project progress and accountability across internal teams, vendors, consultants, and external partners.
• Coordinate project activities to ensure alignment with program objectives, timelines, and deliverables.
• Facilitate project meetings, track action items, and follow through to completion.
• Build and maintain project-level schedules aligned to the broader program master schedule.
• Monitor project dependencies and identify impacts to adjacent workstreams.
• Support integration of project schedules into the overall program plan.
• Escalate schedule risks and conflicts proactively while recommending mitigation strategies.
• Coordinate activities across engineering, design, construction, infrastructure, and operational stakeholders.
• Manage communication between project teams, vendors, consultants, and leadership.
• Ensure all parties understand scope, responsibilities, milestones, and expectations.
• Track project budgets, scope, schedules, risks, issues, and progress.
• Maintain accurate project documentation including meeting notes, decision logs, change requests, and status reports.
• Provide regular project updates and executive-ready reporting to Program Leadership.
• Identify project-level risks, constraints, and issues early.
• Drive resolution through collaboration and ownership.
• Develop mitigation plans and corrective actions when needed.
• Escalate issues appropriately while ensuring accountability remains clear.

Project Manager – Data Center Delivery

• Translate research-grade DS workflows (e.g., proxy metrics, staleness models) into production ML pipelines using Airflow, WandB & Ray while establishing reusable patterns for other teams.
• Apply and productionize causal inference methods using the production ML stack (propensity scoring, IPW, TMLE) to address high-stakes measurement questions beyond experimental capabilities. Build self-serve tooling to empower non-experts to derive rigorous causal insights at scale.
• Partner with ML engineers and product teams to identify opportunities for improved tooling, metrics, and measurement methods, unlocking step-change improvements in model quality and business outcomes.
• Leverage Pinterest's rich metadata and engagement signals to build data-driven frameworks, from feature importance to content deindexing, that improve platform efficiency and speed.
• Design and build centralized ML platform tooling to improve feature and model creation, evaluation, and trust, including production systems that operate daily at scale across all models.

Data Scientist II, ML Infrastructure

• Own the vision, strategy, and roadmap for Bugcrowd’s AI & Data pillar
• Design and scale an automated triage and deduplication capability
• Build the data models and pipelines that aggregate and correlate validated findings
• Develop the logic that diagnoses the highest-impact issues
• Partner with engineering to surface suggestions for action
• Work closely with Agentic Products PM team
• Define and own the metrics to validate the platform works

Staff Product Manager, AI & Data

• Drive the mission forward.
• Build bleeding-edge agentic analytics: Agentic analytics is far from a solved problem, and we want to be the company that solves it. We need the sharpest minds with the curiosity, drive, and focus required to build the tools necessary to bring order and clarity to real world data.
• Define AI impact measurement: own the end-to-end analytics story around adoption, growth, and impact that North leadership uses to prioritize features by measured value rather than customer feedback alone.
• Ship customer-facing analytics: insights that our enterprise customers recognize as evidence of value created.
• Build small language models to categorize, classify, and enrich message data to give customers visibility into how their teams are using our products in their enterprise.
• Design and run experiments: A/B tests, causal inference studies, and opportunity sizing that directly map to product and go-to-market decisions.
• Build predictive models that matter: develop and deploy models for forecasting, segmentation, propensity scoring, and opportunity sizing across Cohere's core business lines.
• Act like an owner: no waiting around. You'll define analytical priorities within your scope, push initiatives from question to production, and own what you build.

• Define and articulate the product vision, strategy, and roadmap for enterprise data platforms in alignment with business goals and technological advancements.
• Lead cross-functional teams through the entire product lifecycle, from concept to launch and beyond, for data platform solutions.
• Conduct market research, competitive analysis, and stakeholder interviews to gather insights and inform product decisions related to data management.
• Prioritize features and initiatives based on stakeholder feedback, business impact, and technical feasibility, making trade-off decisions when needed.
• Drive product development efforts, including defining requirements, managing backlog, and ensuring timely delivery of high-quality data platform releases.
• Collaborate with stakeholders across the organization to ensure alignment and drive consensus on key decisions related to data management and governance.
• Continuously monitor data platform performance, analyze metrics, and iterate on features to optimize user experience and business outcomes.
• Connect with internal customers to regularly understand their data needs and build long-lasting relationships.

Senior Product Manager – Data Products

• Develop best practices for instrumentation and experimentation and communicate those to product engineering teams to help us fulfill our mission - to bring everyone the inspiration to create a life they love
• Bring scientific rigor and statistical methods to the challenges of product creation, development and improvement with an appreciation for the behaviors of our Pinners
• Build and prototype analysis pipelines iteratively to provide insights at scale while developing comprehensive knowledge of data structures and metrics, advocating for changes where needed for product development
• Work cross-functionally to build and communicate key insights, and collaborate closely with product managers, engineers, designers, and researchers to help build the next experiences on Pinterest

Senior Data Scientist, Core

• Lead the strategy, development roadmap, and lifecycle of data products and platforms
• Define the roadmap for data solutions
• Collaborate with cross-functional teams
• Ensure data products are scalable, reliable, and user-friendly
• Monitor and improve data quality, reliability, and user adoption of data tools
• Define and track success metrics for data products

Staff Data Product Manager

• Project Leadership & Delivery
• Lead data-related projects end-to-end: scoping, planning, execution, and delivery
• Build and own project plans, timelines, budgets, and resource allocation across workstreams
• Establish and refine project operating mechanisms -- status cadences, intake processes, and launch readiness criteria
• Remove blockers and make decisions at the pace the project needs to maintain momentum
• Hold workstreams accountable to delivery timelines while protecting space for necessary discovery
• Drive go/no-go decisions for major project milestones and data platform changes
• Data Strategy & Prioritization
• Help shape the organization's data roadmap, prioritizing initiatives based on business value and impact
• Translate business objectives into data project priorities, working with leadership to align investments with strategic goals
• Conduct landscape and options analysis to inform build-vs-buy and tooling decisions from a business-outcomes perspective
• Own the narrative -- communicate project strategy, progress, and outcomes to leadership through status updates and planning documents
• Stakeholder Management & Organizational Influence
• Serve as the primary point of contact between data teams, compliance, engineering, and business stakeholders
• Build and maintain trusted relationships with cross-functional partners; represent project priorities in stakeholder forums
• Manage up effectively -- proactively surface risks, trade-offs, and resource constraints to leadership with recommended paths forward
• Drive alignment across competing organizational needs through influence, data, and clear communication
• Data Governance & Compliance
• Ensure projects adhere to data governance policies, including data quality, access control, retention, and privacy standards
• Partner with data teams and compliance to align project deliverables with governance frameworks (e.g., GDPR, HIPAA, SOC 2, or internal data policies)
• Guide decisions on data ownership, stewardship, and classification across project workstreams
• Ensure the project maintains compliance with organizational data standards and partners effectively with legal and security teams as needed
• Risk Management & Reporting
• Proactively identify risks and dependencies, and drive mitigation plans before they threaten delivery
• Track and report on project and data governance metrics adoption, data quality issues, policy exceptions as part of regular reporting
• Manage scope and change requests, balancing stakeholder needs against delivery constraints

Project Manager, Data Strategy

• Design, build, and scale advanced detection and diagnostic models for solar and storage assets.
• Enable faster detection, fewer truck rolls, and better customer experience.
• Report to the Director of Data Science and partner closely across product management, engineering, and operations to take models from idea to production in Omnidian’s Resolv platform.

Senior Data Scientist

• Define and drive the multi-year product vision for Data Compass Experience and AI.
• Create and maintain a roadmap aligned to Data Compass strategic priorities.
• Own the product strategy and roadmap for Compass AI.
• Lead initiatives including intent-based discovery, multi-agent experiences, chat with metrics, personalized recommendations, ambient AI experiences, and agentic workflows.
• Drive a unified Data Compass experience across catalog, quality, lineage, classification, analytics, and future platform capabilities.
• Enable associates to move seamlessly from question to discovery, trust validation, insight, and action.
• Conduct user research and customer discovery across business and technical audiences.
• Own product requirements, prioritization, roadmap planning, and release execution.

Senior Product Manager, Data Compass Experience – AI

• Own EHR data extraction specifications for the AXIS registry, including data element definitions, data dictionaries, field mappings, and site-specific translation logic across heterogeneous EHR environments.
• Serve as the primary technical liaison to site informatics and IT teams, translating clinical research data requirements into executable data queries and extraction protocols; primary experience with Epic Clarity and/or Caboodle required.
• Design and validate data extraction pipelines that ingest structured EHR data (diagnoses, medications, labs, procedures, utilization) from multiple health systems into a harmonized, analysis-ready format.
• Perform data quality assessment, anomaly detection, and cross-site consistency validation; implement processes to identify and resolve data integrity issues with sites.
• Partner closely with the biostatistics team to deliver clean, structured, well-documented datasets; support data preparation, variable derivation, and dataset specifications for statistical analysis plans.
• Collaborate with clinical operations on site onboarding for clinical site data integration, including support for data governance agreements, BAAs, and technical feasibility assessments.
• Build and maintain the registry data model, data lineage documentation, and codebook to ensure reproducibility and audit-readiness.
• Apply working knowledge of real-world data standards (OMOP CDM, HL7 FHIR, SNOMED, ICD-10, LOINC) to support data harmonization across sites with diverse EHR configurations.
• Support seamless integration between EHR data, the registry EDC, and Karius' standardized internal clinical database.
• Support broader clinical research data analytics across programs, including integrating datasets from multiple sources, building analysis-ready analytic files, and data visualization to support clinical development and biostatistics teams
• Contribute to the design of data collection strategies for future real-world evidence studies and registry expansions.

Senior Clinical Data Scientist

• Coordinate and support execution of the organization's Enterprise Data Strategy, ensuring alignment between business objectives, analytics initiatives, reporting capabilities, and technology roadmaps
• Partner with executive leadership, analytics teams, and Information Technology to develop and advance enterprise data management, governance, and modernization initiatives
• Collaborate with cloud platform teams to support enterprise data and analytics solutions leveraging hyperscaler technologies such as AWS, Azure, and/or Google Cloud Platform (GCP)
• Coordinate enterprise data platform initiatives, including data warehousing, data lake, interoperability, data integration, and advanced analytics programs
• Lead governance and oversight activities related to enterprise data conversions, migrations, and third-party implementation partners
• Facilitate development and adoption of enterprise data standards, metadata management practices, master data management (MDM), and data quality frameworks

Data Strategy Implementation Lead

• Support production systems and help triage issues during live sporting events
• Architect low-latency, real-time analytics systems including raw data collection, feature development and endpoint production
• Build new sports betting data products and predictions offerings
• Integrate large and complex real-time datasets into new consumer and enterprise products
• Develop production-level predictive analytics into enterprise-grade APIs
• Contribute to the design and implementation of new, fully-automated sports data delivery frameworks

Sport Data Scientist

• Translate complex business problems into coherent, actionable quantitative solutions
• Work closely with stakeholders to deeply understand their needs and deliver clear and actionable insights
• Build statistical models that will decipher the impact of key decisions and the impact of competitor’s decisions
• Forecast the future of our DTC businesses
• Measure the price elasticity of our DTC products and competitive products across global and domestic markets
• Develop a coherent and optimized pricing strategy across our DTC products
• Contribute to models advancing Paramount’s DTC financial forecasts
• Integrate data science tooling into all aspects of our DTC financial forecasts

• You'll own the research that helps define our AI Shopping roadmap
• Helping shape how we measure a brand's AI Shopping capability
• Optimizing for the best outcomes
• Designing the studies, building the models, publishing the findings
• Sitting with customers to show them what the data means for their business

Senior Data Scientist, AI Search / AEO Research

• Lead and contribute to the design of observational database analysis, including authoring protocols and analysis plans in support of RWE needs for the MedTech Cardiovascular franchise.
• Lead the synthesis of evidence from observational database analyses with other evidence to inform medical decision-making, including authoring publications and presentations.
• Work closely with colleagues within the department and across the larger cross-sector Epidemiology function, health economics and market access (HEMA) function, clinical function, and other Cardiovascular medical device functions.
• Implement new approaches for working with “big data” in healthcare. Some tasks may include data visualization, predictive analytics and machine learning algorithms.

Associate Director, MedTech Epidemiology, Real World Data Science

• Lead and contribute to the design of observational database analysis, including authoring protocols and analysis plans in support of RWE needs for the MedTech Cardiovascular franchise.
• Lead the synthesis of evidence from observational database analyses with other evidence to inform medical decision-making, including authoring publications and presentations.
• Work closely with colleagues within the department and across the larger cross-sector Epidemiology function, health economics and market access (HEMA) function, clinical function, and other Cardiovascular medical device functions.
• Implement new approaches for working with “big data” in healthcare. Some tasks may include data visualization, predictive analytics and machine learning algorithms.
• The position will report to the Senior Director, MT EPI-RWDS.

• Apply your expertise in quantitative analysis, data mining, and statistical modeling to deliver impactful, objective, and actionable data insights that enable informed business and product decisions
• Drive informed and timely decision-making that improves and optimizes the way our products are created, executed, and adopted
• Collaborate with product managers, engineers, product marketers, and designers
• Think creatively, proactively, and futuristically to identify and size up new opportunities within Snap's long-term roadmap for data-scientific contributions
• Conduct machine learning or statistical analyses, and build pragmatic, scalable, and statistically rigorous solutions to deliver actionable insights, accurate predictions, and effective optimizations
• Communicate best practices in quantitative analysis and develop cross-functional partnerships
• Utilize AI tools to more efficiently develop scalable data solutions, while maintaining rigorous standards for accuracy and statistical integrity.

Senior Data Scientist, Level 5

• Own the analytical foundation for Trust & Safety, including abuse prevalence, fraud loss, false-positive and false-negative rates, time to detect, time to mitigate, appeal and reversal rates, and verification step-up conversion.
• Build reliable datasets and dbt models that connect product events, account and identity signals, payment activity, infrastructure usage, content classifications, enforcement actions, appeals, and support outcomes.
• Develop and evaluate risk models, rules, and anomaly-detection systems for threats such as phishing, scam hosting, cryptomining, token farming, payment fraud, promotional abuse, and AI-agent exploitation.
• Design rigorous offline evaluations, shadow-mode tests, holdouts, and controlled experiments to measure detection quality and the user impact of new policies, enforcement actions, and progressive verification.
• Define thresholds and decision frameworks that balance abuse reduction, economic loss, customer friction, and false positives across free, paid, and enterprise users.
• Investigate emerging abuse patterns, quantify their impact, identify coordinated behavior, and turn ambiguous signals into clear recommendations for product and engineering teams.
• Develop predictive models that estimate account, device, transaction, workspace, or deployment risk and embed those signals into detection, review, and escalation workflows.
• Partner with Support and Legal to improve case review, appeals, reason-code quality, and feedback loops so human decisions become useful model and policy signals.
• Build monitoring that detects model drift, attacker adaptation, data-quality failures, and unexpected harm to legitimate users.
• Communicate findings clearly to technical and non-technical partners, including the tradeoffs, uncertainty, and evidence behind high-impact decisions.

Data Scientist jobs in California

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Manager, Data Cloud Partners – Manufacturing

Project Manager – Data Center Delivery

Data Scientist II, ML Infrastructure

Staff Product Manager, AI & Data

Data Scientist

Senior Product Manager – Data Products

Senior Data Scientist, Core

Staff Data Product Manager

Project Manager, Data Strategy

Senior Data Scientist

Senior Product Manager, Data Compass Experience – AI

Senior Clinical Data Scientist

Data Strategy Implementation Lead

Sport Data Scientist

Senior Data Scientist

Senior Data Scientist, AI Search / AEO Research

Associate Director, MedTech Epidemiology, Real World Data Science

Associate Director, MedTech Epidemiology, Real World Data Science

Senior Data Scientist, Level 5

Data Scientist, Trust & Safety